Fig. 5. (a) Variational autoencoder (VAE) learns materials chemical space under the density reconstruction scheme by explicitly constructing the latent space. Each point in the latent space represents a single material, and thus one can directly generate new materials with optimal functionality. (b) Generative adversarial network (GAN), however, learns materials chemical space under the implicit density prediction scheme which iteratively discriminates the reality of the data generated from the latent space. (c) A VAE-based crystal generative framework proposed by Noh et al.59 using an invertible 3D image representation for the unit cell and basis (adapted with permission from ref. 59 Copyright 2019 Elsevier Inc. Matter).
